ConvDO

Convolutional Differential Operators for Physics-based Deep Learning Study

Calculate the spatial derivative differentiablly!

[📖 Documentation & Examples]

Installation

Install through pip: pip install ConvDO
Install the latest version through pip: pip install git+https://github.com/qiauil/ConvDO
Install locally: Download the repository and run ./install.sh or pip install .

Feature

Positive😀 and negative🙃 things are all features...

PyTorch-based and only supports 2D fields at the moment.
Powered by convolutional neural network.
Differentiable and GPU supported (why not? It's PyTorch based!).
Second order for Dirichlet and Neumann boundary condition.
Up to 8th order for periodic boundary condition.
Obstacles inside of the domain is supported.

Documentations

Check 👉 here

Further Reading

Projects using ConvDO:

Diffusion-based-Flow-Prediction: Diffusion-based flow prediction (DBFP) with uncertainty for airfoils.
To be updated...

If you need to solve more complex PDEs using differentiable functions, please have a check on

PhiFlow: A differentiable PDE solving framework for machine learning
Exponax: Efficient Differentiable n-d PDE solvers in JAX.

For more research on physics based deep learning research, please visit the website of our research group at TUM.